system

Abstract

The system according to this embodiment aims to efficiently propose the optimal design for a custom-built house that reflects the client's wishes. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a modification unit, and a decision unit. The reception unit inputs the land conditions, the surrounding area, and the client's intentions. The proposal unit analyzes the information input by the reception unit and proposes multiple designs. The modification unit adds points to be modified or requests to be made from the designs proposed by the proposal unit and proposes revised designs again. The decision unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently propose an optimal design reflecting the requirements of the client in the design of custom-built houses.

[0005] The system according to the embodiment aims to efficiently propose an optimal design of a custom-built house reflecting the requirements of the client.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a modification unit, and a decision unit. The reception unit receives input on the land conditions, the surrounding area, and the client's intentions. The proposal unit analyzes the information received by the reception unit and proposes multiple designs. The modification unit adds any points that need to be modified or requests that need to be addressed from the designs proposed by the proposal unit and re-proposes a revised design. The decision unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently propose the design of an optimal custom-built house that reflects the client's wishes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the communication I / F (Interface) with a reference sign is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The custom home design system according to an embodiment of the present invention is a system that designs custom homes using an AI agent. This custom home design system allows the user to input information such as the land conditions, surrounding environment, and the client's wishes, and the AI ​​agent analyzes this information to propose multiple designs. The user can then add any points they want modified or requests from the proposed designs, and the AI ​​agent will propose revised designs. If the user is not satisfied with the revised designs, a new design can also be proposed. Finally, the user decides on details such as the floor plan and fixtures, and the design is completed. This system significantly shortens the design period and reduces costs compared to traditional methods of hiring an architect. Furthermore, the AI ​​agent quickly incorporates the client's requests and provides careful advice if they are unreasonable. It also provides an image of the house actually built on the site using CAD, and proposes specialist contractors for renovations, repairs, and cleaning according to the budget. It also coordinates quotes from multiple construction companies and selects the most suitable one within the budget. This allows the client to continue the design process until they are satisfied. For example, the user inputs information such as the land conditions, surrounding environment, and the client's wishes. For example, the AI ​​agent analyzes this information and proposes multiple designs. The user adds any points they want modified or requests from the proposed design, and the AI ​​agent proposes revised designs. If the user does not like the revised designs, they can also request new designs. Finally, the user decides on the details such as the floor plan and fixtures, and the design is completed. This system significantly shortens the design period and reduces costs compared to hiring a traditional architect. The AI ​​agent also quickly incorporates the client's requests and provides careful advice if they are unreasonable. Furthermore, it provides an image of what the house would look like built on the actual site using CAD, and suggests specialist contractors for renovations, repairs, and cleaning according to the budget. It also coordinates quotes from multiple construction companies and selects the best one within the budget. This allows the client to continue designing until they are satisfied. In this way, the custom home design system can quickly and efficiently provide the optimal design that meets the user's needs.

[0029] The custom-built house design system according to this embodiment comprises a reception unit, a proposal unit, a modification unit, and a decision unit. The reception unit receives input on the land conditions, the surrounding area, and the client's intentions. For example, the user can input information such as the land's topography, soil type, and drainage conditions. The reception unit can also receive information such as transportation access around the site, surrounding facilities, and noise levels. Furthermore, the reception unit can receive information such as the client's design preferences, budget, and functional requirements. The proposal unit analyzes the information input by the reception unit and proposes multiple designs. For example, the proposal unit uses AI to generate designs based on the user's requests using data analysis methods and algorithms. The proposal unit can propose multiple designs, including different design concepts and cost variations. The modification unit adds points and requests for modification from the designs proposed by the proposal unit and re-proposes revised designs. For example, the user inputs changes and additional elements to the proposed designs, and the AI ​​generates revised designs based on that input. The modification unit can quickly propose revised designs that meet the user's requests. The decision-making unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit. For example, the user inputs details such as the floor plan, the type of fixtures, and the selection of materials, and the AI ​​determines the final design based on that input. In this way, the custom home design system can input the land conditions, the surrounding environment, and the client's wishes, propose multiple designs, re-propose revised designs, and determine the details such as the floor plan and fixtures.

[0030] The reception desk inputs information about the land, the surrounding area, and the client's intentions. Specifically, users can input information such as the land's topography, soil type, and drainage conditions. Topography information includes whether there is a slope, elevation differences, and the shape of the land. Soil types include classifications such as sandy soil, clayey soil, and loamy soil, and building plans must be tailored to the characteristics of each. Drainage conditions are important information, such as the land's drainage quality and the presence or absence of drainage facilities. The reception desk can also input information about transportation access, surrounding facilities, and noise levels. Transportation access includes the distance to the nearest station or bus stop and the ease of access to major roads. Surrounding facilities consider the location and distance of convenient facilities such as schools, hospitals, supermarkets, and parks. Noise levels are important factors, such as the volume of surrounding traffic and the presence or absence of noise sources such as factories. Furthermore, the reception desk can input information such as the client's design preferences, budget, and functional requirements. The client's design preferences include styles such as modern, classic, and Japanese, and designs must be tailored accordingly. Regarding the budget, a plan that takes into account the overall construction costs and the cost of each component is necessary. Functional requirements include the number of rooms, storage space, and barrier-free design, and the design must be tailored to the client's lifestyle. This allows the reception department to input detailed information about the land conditions, surrounding environment, and client's intentions, and provide this information to the proposal department.

[0031] The proposal department analyzes the information entered by the reception department and proposes multiple designs. Specifically, it uses AI to generate designs based on user requests using data analysis methods and algorithms. Based on information such as the land's topography, soil type, and drainage conditions, the AI ​​proposes the optimal foundation work and building structure. It also considers information such as transportation access, surrounding facilities, and noise levels around the site to create a design suitable for the living environment. For example, in a location with good transportation access, convenience can be enhanced by optimizing the placement of parking spaces and the entrance. Furthermore, it proposes multiple designs, including different design concepts and cost variations, according to the client's design preferences, budget, and functional requirements. The AI ​​can learn from past design data and architectural trends to generate designs that match the client's preferences. For example, it proposes a simple, linear design for a client who prefers modern designs, and a design incorporating decorative elements for a client who prefers classic designs. It also adjusts the grade of materials and equipment used according to the budget to propose a cost-effective design. As a result, the proposal department can quickly propose multiple designs that meet the diverse needs of users.

[0032] The revision department adds any points or requests for revisions to the design proposed by the proposal department and resubmits the revised proposal. Specifically, the user inputs changes and additional elements to the proposed design, and the AI ​​generates a revised proposal based on that input. For example, the user can input requests such as wanting to change the room layout, adjust the window positions, or increase storage space. The AI ​​analyzes these requests and generates the optimal revised proposal. For example, when changing the room layout, the AI ​​proposes the optimal layout while considering the building structure and plumbing arrangement. When adjusting the window positions, the AI ​​proposes the optimal location considering sunlight conditions and ventilation. When increasing storage space, the AI ​​proposes the optimal storage plan considering the size and usability of the room. This allows the revision department to quickly propose revised proposals that meet the user's requests. Furthermore, the revision department can confirm the details of the requests through dialogue with the user and generate more accurate revised proposals. For example, if the user has a specific image in mind, the AI ​​will conduct a detailed interview about that image and generate a revised proposal based on it. In addition, the revision department can increase user satisfaction by making multiple revision proposals. This allows the modification unit to respond flexibly to user requests and propose the optimal design.

[0033] The decision-making unit determines the finer details, such as the floor plan and fixtures, based on the design modified by the modification unit. Specifically, the user inputs details of the floor plan, types of fixtures, and material selections, and the AI ​​determines the final design based on this information. For example, when the user inputs details of the floor plan, they can specify the size and layout of rooms, the location of doors and windows, etc. Based on this information, the AI ​​generates the optimal floor plan. For fixtures, users can select the type and design of doors, windows, and storage. For material selection, users can select materials such as flooring, wall coverings, and ceiling coverings. Based on this information, the AI ​​proposes the most suitable materials and determines the final design. In this way, the decision-making unit can determine the finer details according to the user's requests and complete the final design. Furthermore, the decision-making unit can confirm the final design through dialogue with the user and make fine adjustments as needed. For example, if the user wishes to make fine adjustments to the final design, the AI ​​will listen to their requests in detail and make adjustments based on that. The decision-making unit can also create a construction plan and cost estimate based on the final design. This allows the decision-making unit to provide the optimal design according to the user's requests, and to smoothly proceed with the design process for custom-built homes by creating construction plans and cost estimates.

[0034] The proposal department can quickly incorporate the client's requests and provide careful advice if they are unreasonable. For example, the proposal department can use AI to provide real-time feedback and immediately reflect the client's requests. Furthermore, the proposal department can present alternative solutions and provide detailed explanations for requests that are technically impossible or exceed the budget. For example, if the design desired by the client is technically impossible to realize, the proposal department will propose a feasible alternative design. Also, if the client's request exceeds the budget, the proposal department can present alternative solutions that are feasible within the budget. In this way, the proposal department can quickly incorporate the client's requests and provide careful advice if they are unreasonable.

[0035] The proposal team can provide an image of the structure actually built on the site using CAD. For example, the proposal team can create a 3D model and provide the user with an image using virtual reality. Furthermore, the proposal team can provide detailed 3D models so that users can visually confirm the design. For example, the proposal team can provide a virtual tour for users to review the design. The proposal team can also provide an interactive 3D model for users to review the design. This allows the proposal team to provide an image of the structure actually built on the site using CAD.

[0036] The proposal department can also propose specialist contractors for renovations, repairs, and cleaning, tailored to the client's budget. For example, the proposal department can conduct cost simulations and propose specialist contractors that fit the client's budget. Furthermore, the proposal department can analyze cost-effectiveness and propose the most suitable specialist contractor. For instance, the proposal department can conduct cost simulations for renovations and propose renovation contractors that fit the client's budget. Similarly, the proposal department can analyze the cost-effectiveness of repairs and propose the most suitable repair contractor. In this way, the proposal department can propose specialist contractors for renovations, repairs, and cleaning, tailored to the client's budget.

[0037] The proposal department can coordinate quotes from multiple construction companies and select the best one within the budget. For example, the proposal department can negotiate prices and select the best construction company within the client's budget. Furthermore, the proposal department can compare quotes and select the best construction company. For example, the proposal department can compare quotes from multiple construction companies and select the best one within the client's budget. The proposal department can also select the best construction company based on past performance and evaluations. In this way, the proposal department can coordinate quotes from multiple construction companies and select the best one within the budget.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the land conditions and client preferences that the user has frequently entered in the past. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze the user's past input history and suggest the most suitable input method.

[0039] The input system can filter input content based on the user's current living situation and areas of interest. For example, when a user enters their family structure, the system can make appropriate suggestions based on the ages and lifestyles of the family members. Furthermore, when a user enters their hobbies or areas of interest, the system can prioritize displaying design elements related to those hobbies. Additionally, when a user enters their current living situation (e.g., whether they have pets), the system can suggest design elements accordingly. This allows the system to filter input content based on the user's current living situation and areas of interest.

[0040] The input system can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user lives in a specific area, the input system will prioritize displaying information related to that area. Furthermore, if a user is interested in a specific area, the input system can prioritize displaying design elements related to that area. Additionally, if a user is considering moving to a specific area, the input system can prioritize displaying information about the living environment in that area. This allows the input system to prioritize inputting highly relevant information by considering the user's geographical location.

[0041] The reception desk can analyze the user's social media activity and input relevant information during the input process. For example, the reception desk can suggest relevant design elements based on information the user has shared on social media. It can also suggest relevant design elements based on information about accounts the user follows on social media. Furthermore, it can suggest relevant design elements based on information the user has shown interest in on social media. This allows the reception desk to analyze the user's social media activity and input relevant information.

[0042] The proposal team can adjust the level of detail in their proposals based on the importance of the design elements. For example, they can provide detailed explanations and multiple options for important design elements, and concise explanations and fewer options for less important elements. Furthermore, they can provide detailed explanations and customizable options for design elements of particular interest to the user. This allows the proposal team to adjust the level of detail in their proposals based on the importance of the design elements.

[0043] The proposal function can apply different proposal algorithms depending on the design category. For example, for the design of living spaces, it can apply a proposal algorithm that emphasizes comfort and functionality. For exterior design, it can apply a proposal algorithm that emphasizes aesthetics and durability. Furthermore, for energy efficiency, it can apply a proposal algorithm that emphasizes environmental impact and cost efficiency. In this way, the proposal function can apply different proposal algorithms depending on the design category.

[0044] The proposal department can prioritize proposals based on the design submission deadlines. For example, the proposal department can prioritize proposals for design elements that are urgent. Furthermore, the proposal department can quickly submit proposals for design elements with approaching deadlines. In addition, the proposal department can prioritize proposals for design elements that users are particularly interested in. This allows the proposal department to prioritize proposals based on the design submission deadlines.

[0045] The proposal department can adjust the order of proposals based on the relevance of the design. For example, it can prioritize proposals for highly relevant design elements. It can also postpone proposals for less relevant design elements. Furthermore, it can prioritize proposals for design elements that the user is particularly interested in. In this way, the proposal department can adjust the order of proposals based on the relevance of the design.

[0046] The editing unit can analyze the user's past editing history and propose the optimal editing method. For example, it can propose the optimal editing method based on the user's past editing history. Furthermore, it can prioritize suggesting frequently edited elements based on the user's past editing history. In addition, it can analyze the user's past editing history and propose the most efficient editing method. Thus, the editing unit can analyze the user's past editing history and propose the optimal editing method.

[0047] The modification unit can customize the modifications based on the user's current living situation. For example, if the user changes their family structure, the modification unit can suggest corresponding modifications. Furthermore, if the user changes their lifestyle, the modification unit can suggest corresponding modifications. In addition, if the user starts a new hobby, the modification unit can suggest corresponding modifications. This allows the modification unit to customize the modifications based on the user's current living situation.

[0048] The editing function can select the optimal editing method while considering the user's geographical location. For example, if the user lives in a specific region, the editing function can prioritize displaying editing content related to that region. Furthermore, if the user is interested in a specific region, the editing function can prioritize displaying editing content related to that region. In addition, if the user is considering moving to a specific region, the editing function can prioritize displaying editing content related to the living environment of that region. This allows the editing function to select the optimal editing method while considering the user's geographical location.

[0049] The revision unit can analyze the user's social media activity and suggest revisions during the revision process. For example, the revision unit can suggest relevant revisions based on information the user has shared on social media. It can also suggest relevant revisions based on information about accounts the user follows on social media. Furthermore, it can suggest relevant revisions based on information the user has shown interest in on social media. In this way, the revision unit can analyze the user's social media activity and suggest revisions.

[0050] The decision-making unit can analyze the user's past decision history and propose the optimal decision-making method at the time of decision-making. For example, the decision-making unit can propose the optimal decision-making method based on the user's past decisions. Furthermore, the decision-making unit can prioritize and propose elements that are frequently decided based on the user's past decision history. In addition, the decision-making unit can analyze the user's past decision history and propose the most efficient decision-making method. In this way, the decision-making unit can analyze the user's past decision history and propose the optimal decision-making method.

[0051] The decision-making unit can customize the decision based on the user's current living situation. For example, if the user changes their family structure, the unit can suggest a corresponding decision. Furthermore, if the user changes their lifestyle, the unit can suggest a corresponding decision. In addition, if the user starts a new hobby, the unit can suggest a corresponding decision. This allows the decision-making unit to customize the decision based on the user's current living situation.

[0052] The decision-making unit can select the optimal decision method by considering the user's geographical location information at the time of decision-making. For example, if the user lives in a specific region, the decision-making unit can prioritize displaying decision content related to that region. Furthermore, if the user is interested in a specific region, the decision-making unit can prioritize displaying decision content related to the living environment of that region. In this way, the decision-making unit can select the optimal decision method by considering the user's geographical location information.

[0053] The decision-making unit can analyze the user's social media activity and propose a decision at the time of decision-making. For example, the decision-making unit can propose relevant decisions based on information the user has shared on social media. It can also propose relevant decisions based on information about accounts the user follows on social media. Furthermore, it can propose relevant decisions based on information the user has shown interest in on social media. In this way, the decision-making unit can analyze the user's social media activity and propose a decision.

[0054] The proposal department can analyze the user's past request history to propose the most suitable advice. For example, the proposal department can propose the most suitable advice based on the user's past requests. Furthermore, the proposal department can prioritize frequently requested elements based on the user's past request history. In addition, the proposal department can analyze the user's past request history to propose the most efficient advice. This allows the proposal department to analyze the user's past request history and propose the most suitable advice.

[0055] The suggestion function can select the most appropriate advice by considering the user's geographical location. For example, if the user lives in a specific region, the suggestion function will prioritize displaying advice related to that region. Furthermore, if the user is interested in a specific region, the suggestion function can prioritize displaying advice related to that region. In addition, if the user is considering moving to a specific region, the suggestion function can prioritize displaying advice about the living environment in that region. This allows the suggestion function to select the most appropriate advice by considering the user's geographical location.

[0056] The suggestion unit can analyze the user's past image history when providing images and propose the optimal display method. For example, the suggestion unit can propose the optimal display method based on the content of images the user has previously viewed. Furthermore, the suggestion unit can prioritize and propose elements that are frequently displayed based on the user's past image history. In addition, the suggestion unit can analyze the user's past image history and propose the most efficient display method. As a result, the suggestion unit can analyze the user's past image history and propose the optimal display method.

[0057] The suggestion function can select the optimal display method when providing images, taking into account the user's geographical location. For example, if the user lives in a specific region, the suggestion function can prioritize displaying images related to that region. Furthermore, if the user is interested in a specific region, the suggestion function can prioritize displaying images related to that region. In addition, if the user is considering moving to a specific region, the suggestion function can prioritize displaying images related to the living environment of that region. This allows the suggestion function to select the optimal display method, taking into account the user's geographical location.

[0058] The proposal department can analyze the user's past vendor usage history to provide the most suitable proposal when suggesting vendors. For example, the proposal department can suggest the most suitable vendor based on the vendors the user has used in the past. Furthermore, the proposal department can prioritize suggesting vendors that the user frequently uses based on their past vendor usage history. In addition, the proposal department can analyze the user's past vendor usage history to suggest the most efficient vendor. In this way, the proposal department can analyze the user's past vendor usage history to provide the most suitable proposal.

[0059] The proposal function can select the most suitable service provider by considering the user's geographical location when proposing service providers. For example, if the user lives in a specific region, the proposal function can prioritize displaying service providers related to that region. Furthermore, if the user is interested in a specific region, the proposal function can prioritize displaying service providers related to the living environment of that region. In this way, the proposal function can select the most suitable service provider by considering the user's geographical location.

[0060] The proposal department can analyze the user's past estimation history and propose the optimal adjustment method when adjusting estimates. For example, the proposal department can propose the optimal adjustment method based on the user's past estimations. Furthermore, the proposal department can prioritize and propose elements that are frequently adjusted based on the user's past estimation history. In addition, the proposal department can analyze the user's past estimation history and propose the most efficient adjustment method. This allows the proposal department to analyze the user's past estimation history and propose the optimal adjustment method.

[0061] The proposal function can select the most suitable construction company by considering the user's geographical location when adjusting estimates. For example, if the user lives in a specific area, the proposal function will prioritize displaying construction companies related to that area. Furthermore, if the user is interested in a specific area, the proposal function can prioritize displaying construction companies related to that area. In addition, if the user is considering moving to a specific area, the proposal function can prioritize displaying construction companies related to the living environment of that area. This allows the proposal function to select the most suitable construction company by considering the user's geographical location.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can make similar proposals based on designs and requests the user has previously preferred. It can also make more appropriate proposals by avoiding proposals the user has previously rejected. Furthermore, it can predict and propose proposal methods preferred at specific times based on the user's past proposal history. This allows the proposal department to analyze a user's past proposal history and select the most suitable proposal method.

[0064] The proposal department can suggest region-specific design elements by considering the user's geographical location. For example, if the user lives in a cold region, it can suggest a design with high insulation performance. If the user lives in a humid region, it can suggest a design with moisture countermeasures. Furthermore, if the user lives in an earthquake-prone region, it can suggest a design with high seismic resistance performance. In this way, the proposal department can suggest region-specific design elements by considering the user's geographical location.

[0065] The suggestion department can analyze a user's past suggestion history and determine the priority of suggestions. For example, it can prioritize suggestions that the user has frequently selected in the past. It can also postpone suggestions that the user has previously rejected, thereby providing more appropriate suggestions. Furthermore, it can predict and prioritize suggestions that are preferred during specific time periods based on the user's past suggestion history. In this way, the suggestion department can analyze a user's past suggestion history and determine the priority of suggestions.

[0066] The suggestion function can customize its suggestions based on the user's current living situation. For example, if the user changes their family structure, it can provide suggestions accordingly. Similarly, if the user changes their lifestyle, it can provide suggestions accordingly. Furthermore, if the user starts a new hobby, it can provide suggestions accordingly. In this way, the suggestion function can customize its suggestions based on the user's current living situation.

[0067] The proposal department can analyze users' social media activity and incorporate relevant information into its proposals. For example, it can propose relevant design elements based on information users share on social media. It can also propose relevant design elements based on information about accounts users follow on social media. Furthermore, it can propose relevant design elements based on information users have shown interest in on social media. In this way, the proposal department can analyze users' social media activity and incorporate relevant information into its proposals.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk inputs information about the land, the surrounding area, and the client's intentions. For example, the user can input information such as the land's topography, soil type, drainage conditions, transportation access, surrounding facilities, noise level, design preferences, budget, and functional requirements. Step 2: The proposal department analyzes the information entered by the reception department and proposes multiple designs. For example, it uses AI to generate multiple designs that include different design concepts and cost variations based on user requests, employing data analysis methods and algorithms. Step 3: The revision team adds any points or requests for changes to the design proposed by the proposal team and resubmits the revised proposal. For example, the user inputs changes or additional elements to the proposed design, and the AI ​​generates a revised proposal based on that input. Step 4: The decision-making unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit. For example, the user inputs details of the floor plan, types of fixtures, material selections, etc., and the AI ​​determines the final design based on that information.

[0070] (Example of form 2) The custom home design system according to an embodiment of the present invention is a system that designs custom homes using an AI agent. This custom home design system allows the user to input information such as the land conditions, surrounding environment, and the client's wishes, and the AI ​​agent analyzes this information to propose multiple designs. The user can then add any points they want modified or requests from the proposed designs, and the AI ​​agent will propose revised designs. If the user is not satisfied with the revised designs, a new design can also be proposed. Finally, the user decides on details such as the floor plan and fixtures, and the design is completed. This system significantly shortens the design period and reduces costs compared to traditional methods of hiring an architect. Furthermore, the AI ​​agent quickly incorporates the client's requests and provides careful advice if they are unreasonable. It also provides an image of the house actually built on the site using CAD, and proposes specialist contractors for renovations, repairs, and cleaning according to the budget. It also coordinates quotes from multiple construction companies and selects the most suitable one within the budget. This allows the client to continue the design process until they are satisfied. For example, the user inputs information such as the land conditions, surrounding environment, and the client's wishes. For example, the AI ​​agent analyzes this information and proposes multiple designs. The user adds any points they want modified or requests from the proposed design, and the AI ​​agent proposes revised designs. If the user does not like the revised designs, they can also request new designs. Finally, the user decides on the details such as the floor plan and fixtures, and the design is completed. This system significantly shortens the design period and reduces costs compared to hiring a traditional architect. The AI ​​agent also quickly incorporates the client's requests and provides careful advice if they are unreasonable. Furthermore, it provides an image of what the house would look like built on the actual site using CAD, and suggests specialist contractors for renovations, repairs, and cleaning according to the budget. It also coordinates quotes from multiple construction companies and selects the best one within the budget. This allows the client to continue designing until they are satisfied. In this way, the custom home design system can quickly and efficiently provide the optimal design that meets the user's needs.

[0071] The custom-built house design system according to this embodiment comprises a reception unit, a proposal unit, a modification unit, and a decision unit. The reception unit receives input on the land conditions, the surrounding area, and the client's intentions. For example, the user can input information such as the land's topography, soil type, and drainage conditions. The reception unit can also receive information such as transportation access around the site, surrounding facilities, and noise levels. Furthermore, the reception unit can receive information such as the client's design preferences, budget, and functional requirements. The proposal unit analyzes the information input by the reception unit and proposes multiple designs. For example, the proposal unit uses AI to generate designs based on the user's requests using data analysis methods and algorithms. The proposal unit can propose multiple designs, including different design concepts and cost variations. The modification unit adds points and requests for modification from the designs proposed by the proposal unit and re-proposes revised designs. For example, the user inputs changes and additional elements to the proposed designs, and the AI ​​generates revised designs based on that input. The modification unit can quickly propose revised designs that meet the user's requests. The decision-making unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit. For example, the user inputs details such as the floor plan, the type of fixtures, and the selection of materials, and the AI ​​determines the final design based on that input. In this way, the custom home design system can input the land conditions, the surrounding environment, and the client's wishes, propose multiple designs, re-propose revised designs, and determine the details such as the floor plan and fixtures.

[0072] The reception desk inputs information about the land, the surrounding area, and the client's intentions. Specifically, users can input information such as the land's topography, soil type, and drainage conditions. Topography information includes whether there is a slope, elevation differences, and the shape of the land. Soil types include classifications such as sandy soil, clayey soil, and loamy soil, and building plans must be tailored to the characteristics of each. Drainage conditions are important information, such as the land's drainage quality and the presence or absence of drainage facilities. The reception desk can also input information about transportation access, surrounding facilities, and noise levels. Transportation access includes the distance to the nearest station or bus stop and the ease of access to major roads. Surrounding facilities consider the location and distance of convenient facilities such as schools, hospitals, supermarkets, and parks. Noise levels are important factors, such as the volume of surrounding traffic and the presence or absence of noise sources such as factories. Furthermore, the reception desk can input information such as the client's design preferences, budget, and functional requirements. The client's design preferences include styles such as modern, classic, and Japanese, and designs must be tailored accordingly. Regarding the budget, a plan that takes into account the overall construction costs and the cost of each component is necessary. Functional requirements include the number of rooms, storage space, and barrier-free design, and the design must be tailored to the client's lifestyle. This allows the reception department to input detailed information about the land conditions, surrounding environment, and client's intentions, and provide this information to the proposal department.

[0073] The proposal department analyzes the information entered by the reception department and proposes multiple designs. Specifically, it uses AI to generate designs based on user requests using data analysis methods and algorithms. Based on information such as the land's topography, soil type, and drainage conditions, the AI ​​proposes the optimal foundation work and building structure. It also considers information such as transportation access, surrounding facilities, and noise levels around the site to create a design suitable for the living environment. For example, in a location with good transportation access, convenience can be enhanced by optimizing the placement of parking spaces and the entrance. Furthermore, it proposes multiple designs, including different design concepts and cost variations, according to the client's design preferences, budget, and functional requirements. The AI ​​can learn from past design data and architectural trends to generate designs that match the client's preferences. For example, it proposes a simple, linear design for a client who prefers modern designs, and a design incorporating decorative elements for a client who prefers classic designs. It also adjusts the grade of materials and equipment used according to the budget to propose a cost-effective design. As a result, the proposal department can quickly propose multiple designs that meet the diverse needs of users.

[0074] The revision department adds any points or requests for revisions to the design proposed by the proposal department and resubmits the revised proposal. Specifically, the user inputs changes and additional elements to the proposed design, and the AI ​​generates a revised proposal based on that input. For example, the user can input requests such as wanting to change the room layout, adjust the window positions, or increase storage space. The AI ​​analyzes these requests and generates the optimal revised proposal. For example, when changing the room layout, the AI ​​proposes the optimal layout while considering the building structure and plumbing arrangement. When adjusting the window positions, the AI ​​proposes the optimal location considering sunlight conditions and ventilation. When increasing storage space, the AI ​​proposes the optimal storage plan considering the size and usability of the room. This allows the revision department to quickly propose revised proposals that meet the user's requests. Furthermore, the revision department can confirm the details of the requests through dialogue with the user and generate more accurate revised proposals. For example, if the user has a specific image in mind, the AI ​​will conduct a detailed interview about that image and generate a revised proposal based on it. In addition, the revision department can increase user satisfaction by making multiple revision proposals. This allows the modification unit to respond flexibly to user requests and propose the optimal design.

[0075] The decision-making unit determines the finer details, such as the floor plan and fixtures, based on the design modified by the modification unit. Specifically, the user inputs details of the floor plan, types of fixtures, and material selections, and the AI ​​determines the final design based on this information. For example, when the user inputs details of the floor plan, they can specify the size and layout of rooms, the location of doors and windows, etc. Based on this information, the AI ​​generates the optimal floor plan. For fixtures, users can select the type and design of doors, windows, and storage. For material selection, users can select materials such as flooring, wall coverings, and ceiling coverings. Based on this information, the AI ​​proposes the most suitable materials and determines the final design. In this way, the decision-making unit can determine the finer details according to the user's requests and complete the final design. Furthermore, the decision-making unit can confirm the final design through dialogue with the user and make fine adjustments as needed. For example, if the user wishes to make fine adjustments to the final design, the AI ​​will listen to their requests in detail and make adjustments based on that. The decision-making unit can also create a construction plan and cost estimate based on the final design. This allows the decision-making unit to provide the optimal design according to the user's requests, and to smoothly proceed with the design process for custom-built homes by creating construction plans and cost estimates.

[0076] The proposal department can quickly incorporate the client's requests and provide careful advice if they are unreasonable. For example, the proposal department can use AI to provide real-time feedback and immediately reflect the client's requests. Furthermore, the proposal department can present alternative solutions and provide detailed explanations for requests that are technically impossible or exceed the budget. For example, if the design desired by the client is technically impossible to realize, the proposal department will propose a feasible alternative design. Also, if the client's request exceeds the budget, the proposal department can present alternative solutions that are feasible within the budget. In this way, the proposal department can quickly incorporate the client's requests and provide careful advice if they are unreasonable.

[0077] The proposal team can provide an image of the structure actually built on the site using CAD. For example, the proposal team can create a 3D model and provide the user with an image using virtual reality. Furthermore, the proposal team can provide detailed 3D models so that users can visually confirm the design. For example, the proposal team can provide a virtual tour for users to review the design. The proposal team can also provide an interactive 3D model for users to review the design. This allows the proposal team to provide an image of the structure actually built on the site using CAD.

[0078] The proposal department can also propose specialist contractors for renovations, repairs, and cleaning, tailored to the client's budget. For example, the proposal department can conduct cost simulations and propose specialist contractors that fit the client's budget. Furthermore, the proposal department can analyze cost-effectiveness and propose the most suitable specialist contractor. For instance, the proposal department can conduct cost simulations for renovations and propose renovation contractors that fit the client's budget. Similarly, the proposal department can analyze the cost-effectiveness of repairs and propose the most suitable repair contractor. In this way, the proposal department can propose specialist contractors for renovations, repairs, and cleaning, tailored to the client's budget.

[0079] The proposal department can coordinate quotes from multiple construction companies and select the best one within the budget. For example, the proposal department can negotiate prices and select the best construction company within the client's budget. Furthermore, the proposal department can compare quotes and select the best construction company. For example, the proposal department can compare quotes from multiple construction companies and select the best one within the client's budget. The proposal department can also select the best construction company based on past performance and evaluations. In this way, the proposal department can coordinate quotes from multiple construction companies and select the best one within the budget.

[0080] The reception system can estimate the user's emotions and adjust the timing and method of input based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick input. This allows the reception system to adjust the timing and method of input based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the land conditions and client preferences that the user has frequently entered in the past. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze the user's past input history and suggest the most suitable input method.

[0082] The input system can filter input content based on the user's current living situation and areas of interest. For example, when a user enters their family structure, the system can make appropriate suggestions based on the ages and lifestyles of the family members. Furthermore, when a user enters their hobbies or areas of interest, the system can prioritize displaying design elements related to those hobbies. Additionally, when a user enters their current living situation (e.g., whether they have pets), the system can suggest design elements accordingly. This allows the system to filter input content based on the user's current living situation and areas of interest.

[0083] The reception system can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception system will prioritize displaying important input items and postpone other items. If the user is relaxed, the reception system can display all input items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the reception system can display the most important input items first, enabling quick completion. In this way, the reception system can prioritize input based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The input system can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user lives in a specific area, the input system will prioritize displaying information related to that area. Furthermore, if a user is interested in a specific area, the input system can prioritize displaying design elements related to that area. Additionally, if a user is considering moving to a specific area, the input system can prioritize displaying information about the living environment in that area. This allows the input system to prioritize inputting highly relevant information by considering the user's geographical location.

[0085] The reception desk can analyze the user's social media activity and input relevant information during the input process. For example, the reception desk can suggest relevant design elements based on information the user has shared on social media. It can also suggest relevant design elements based on information about accounts the user follows on social media. Furthermore, it can suggest relevant design elements based on information the user has shown interest in on social media. This allows the reception desk to analyze the user's social media activity and input relevant information.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions, allowing the user to carefully consider their options. If the user is in a hurry, the suggestion unit can provide concise suggestions, enabling a quick decision. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions to capture the user's interest. In this way, the suggestion unit can adjust the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The proposal team can adjust the level of detail in their proposals based on the importance of the design elements. For example, they can provide detailed explanations and multiple options for important design elements, and concise explanations and fewer options for less important elements. Furthermore, they can provide detailed explanations and customizable options for design elements of particular interest to the user. This allows the proposal team to adjust the level of detail in their proposals based on the importance of the design elements.

[0088] The proposal function can apply different proposal algorithms depending on the design category. For example, for the design of living spaces, it can apply a proposal algorithm that emphasizes comfort and functionality. For exterior design, it can apply a proposal algorithm that emphasizes aesthetics and durability. Furthermore, for energy efficiency, it can apply a proposal algorithm that emphasizes environmental impact and cost efficiency. In this way, the proposal function can apply different proposal algorithms depending on the design category.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions, allowing the user to carefully consider their options. If the user is in a hurry, the suggestion unit can provide concise suggestions, enabling a quick decision. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions to capture the user's interest. This allows the suggestion unit to adjust the length of its suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The proposal department can prioritize proposals based on the design submission deadlines. For example, the proposal department can prioritize proposals for design elements that are urgent. Furthermore, the proposal department can quickly submit proposals for design elements with approaching deadlines. In addition, the proposal department can prioritize proposals for design elements that users are particularly interested in. This allows the proposal department to prioritize proposals based on the design submission deadlines.

[0091] The proposal department can adjust the order of proposals based on the relevance of the design. For example, it can prioritize proposals for highly relevant design elements. It can also postpone proposals for less relevant design elements. Furthermore, it can prioritize proposals for design elements that the user is particularly interested in. In this way, the proposal department can adjust the order of proposals based on the relevance of the design.

[0092] The editing unit can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is relaxed, the editing unit can suggest detailed editing options, allowing the user to carefully consider the choices. If the user is in a hurry, the editing unit can suggest concise editing options, allowing for a quick selection. Furthermore, if the user is excited, the editing unit can suggest visually appealing editing options to capture the user's interest. In this way, the editing unit can adjust the editing method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The editing unit can analyze the user's past editing history and propose the optimal editing method. For example, it can propose the optimal editing method based on the user's past editing history. Furthermore, it can prioritize suggesting frequently edited elements based on the user's past editing history. In addition, it can analyze the user's past editing history and propose the most efficient editing method. Thus, the editing unit can analyze the user's past editing history and propose the optimal editing method.

[0094] The modification unit can customize the modifications based on the user's current living situation. For example, if the user changes their family structure, the modification unit can suggest corresponding modifications. Furthermore, if the user changes their lifestyle, the modification unit can suggest corresponding modifications. In addition, if the user starts a new hobby, the modification unit can suggest corresponding modifications. This allows the modification unit to customize the modifications based on the user's current living situation.

[0095] The editing unit can estimate the user's emotions and determine the priority of edits based on those emotions. For example, if the user is stressed, the editing unit can prioritize displaying important edits and postpone others. If the user is relaxed, the editing unit can display all edits equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the editing unit can display the most important edits first, enabling quick completion of the edits. In this way, the editing unit can determine the priority of edits based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The editing function can select the optimal editing method while considering the user's geographical location. For example, if the user lives in a specific region, the editing function can prioritize displaying editing content related to that region. Furthermore, if the user is interested in a specific region, the editing function can prioritize displaying editing content related to that region. In addition, if the user is considering moving to a specific region, the editing function can prioritize displaying editing content related to the living environment of that region. This allows the editing function to select the optimal editing method while considering the user's geographical location.

[0097] The revision unit can analyze the user's social media activity and suggest revisions during the revision process. For example, the revision unit can suggest relevant revisions based on information the user has shared on social media. It can also suggest relevant revisions based on information about accounts the user follows on social media. Furthermore, it can suggest relevant revisions based on information the user has shown interest in on social media. In this way, the revision unit can analyze the user's social media activity and suggest revisions.

[0098] The decision-making unit can estimate the user's emotions and adjust its decision-making process based on those emotions. For example, if the user is relaxed, the unit can provide detailed explanations to allow the user to fully consider the options. If the user is in a hurry, the unit can provide concise explanations to enable a quick choice. Furthermore, if the user is excited, the unit can provide visually appealing explanations to capture the user's interest. In this way, the decision-making unit can adjust its decision-making process based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The decision-making unit can analyze the user's past decision history and propose the optimal decision-making method at the time of decision-making. For example, the decision-making unit can propose the optimal decision-making method based on the user's past decisions. Furthermore, the decision-making unit can prioritize and propose elements that are frequently decided based on the user's past decision history. In addition, the decision-making unit can analyze the user's past decision history and propose the most efficient decision-making method. In this way, the decision-making unit can analyze the user's past decision history and propose the optimal decision-making method.

[0100] The decision-making unit can customize the decision based on the user's current living situation. For example, if the user changes their family structure, the unit can suggest a corresponding decision. Furthermore, if the user changes their lifestyle, the unit can suggest a corresponding decision. In addition, if the user starts a new hobby, the unit can suggest a corresponding decision. This allows the decision-making unit to customize the decision based on the user's current living situation.

[0101] The decision-making unit can estimate the user's emotions and prioritize decisions based on those emotions. For example, if the user is stressed, the unit can prioritize displaying important decision items and postpone other items. If the user is relaxed, the unit can display all decision items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the unit can display the most important decision items first, enabling them to complete decisions quickly. In this way, the decision-making unit can prioritize decisions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The decision-making unit can select the optimal decision method by considering the user's geographical location information at the time of decision-making. For example, if the user lives in a specific region, the decision-making unit can prioritize displaying decision content related to that region. Furthermore, if the user is interested in a specific region, the decision-making unit can prioritize displaying decision content related to the living environment of that region. In this way, the decision-making unit can select the optimal decision method by considering the user's geographical location information.

[0103] The decision-making unit can analyze the user's social media activity and propose a decision at the time of decision-making. For example, the decision-making unit can propose relevant decisions based on information the user has shared on social media. It can also propose relevant decisions based on information about accounts the user follows on social media. Furthermore, it can propose relevant decisions based on information the user has shown interest in on social media. In this way, the decision-making unit can analyze the user's social media activity and propose a decision.

[0104] The suggestion unit can estimate the user's emotions and adjust its advice based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed advice to allow the user to carefully consider their options. If the user is in a hurry, the suggestion unit can provide concise advice to enable a quick decision. Furthermore, if the user is excited, the suggestion unit can provide visually appealing advice to capture the user's interest. In this way, the suggestion unit can adjust its advice based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The proposal department can analyze the user's past request history to propose the most suitable advice. For example, the proposal department can propose the most suitable advice based on the user's past requests. Furthermore, the proposal department can prioritize frequently requested elements based on the user's past request history. In addition, the proposal department can analyze the user's past request history to propose the most efficient advice. This allows the proposal department to analyze the user's past request history and propose the most suitable advice.

[0106] The suggestion unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important advice items and postpone others. If the user is relaxed, the suggestion unit can display all advice items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the suggestion unit can display the most important advice items first, allowing the user to complete the advice quickly. In this way, the suggestion unit can prioritize advice based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The suggestion function can select the most appropriate advice by considering the user's geographical location. For example, if the user lives in a specific region, the suggestion function will prioritize displaying advice related to that region. Furthermore, if the user is interested in a specific region, the suggestion function can prioritize displaying advice related to that region. In addition, if the user is considering moving to a specific region, the suggestion function can prioritize displaying advice about the living environment in that region. This allows the suggestion function to select the most appropriate advice by considering the user's geographical location.

[0108] The suggestion unit can estimate the user's emotions and adjust how images are displayed based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed images to allow the user to carefully consider their options. If the user is in a hurry, the suggestion unit can provide concise images to allow for quick selection. Furthermore, if the user is excited, the suggestion unit can provide visually appealing images to capture the user's interest. In this way, the suggestion unit can adjust how images are displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The suggestion unit can analyze the user's past image history when providing images and propose the optimal display method. For example, the suggestion unit can propose the optimal display method based on the content of images the user has previously viewed. Furthermore, the suggestion unit can prioritize and propose elements that are frequently displayed based on the user's past image history. In addition, the suggestion unit can analyze the user's past image history and propose the most efficient display method. As a result, the suggestion unit can analyze the user's past image history and propose the optimal display method.

[0110] The suggestion unit can estimate the user's emotions and prioritize images based on those emotions. For example, if the user is stressed, the suggestion unit can prioritize displaying important image items and postpone other items. If the user is relaxed, the suggestion unit can display all image items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the suggestion unit can display the most important image items first, allowing the user to complete the image quickly. In this way, the suggestion unit can prioritize images based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The suggestion function can select the optimal display method when providing images, taking into account the user's geographical location. For example, if the user lives in a specific region, the suggestion function can prioritize displaying images related to that region. Furthermore, if the user is interested in a specific region, the suggestion function can prioritize displaying images related to that region. In addition, if the user is considering moving to a specific region, the suggestion function can prioritize displaying images related to the living environment of that region. This allows the suggestion function to select the optimal display method, taking into account the user's geographical location.

[0112] The suggestion function can estimate the user's emotions and adjust the way it suggests vendors based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed vendor information, allowing the user to carefully consider their options. If the user is in a hurry, the suggestion function can provide concise vendor information, enabling a quick selection. Furthermore, if the user is excited, the suggestion function can provide visually appealing vendor information to capture the user's interest. This allows the suggestion function to adjust how it suggests vendors based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The proposal department can analyze the user's past vendor usage history to provide the most suitable proposal when suggesting vendors. For example, the proposal department can suggest the most suitable vendor based on the vendors the user has used in the past. Furthermore, the proposal department can prioritize suggesting vendors that the user frequently uses based on their past vendor usage history. In addition, the proposal department can analyze the user's past vendor usage history to suggest the most efficient vendor. In this way, the proposal department can analyze the user's past vendor usage history to provide the most suitable proposal.

[0114] The recommendation system can estimate the user's emotions and prioritize vendors based on those emotions. For example, if the user is stressed, the recommendation system will prioritize displaying important vendor information and delay other information. If the user is relaxed, the recommendation system can display all vendor information equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the recommendation system can display the most important vendor information first, allowing for quick selection. In this way, the recommendation system can prioritize vendors based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The proposal function can select the most suitable service provider by considering the user's geographical location when proposing service providers. For example, if the user lives in a specific region, the proposal function can prioritize displaying service providers related to that region. Furthermore, if the user is interested in a specific region, the proposal function can prioritize displaying service providers related to the living environment of that region. In this way, the proposal function can select the most suitable service provider by considering the user's geographical location.

[0116] The suggestion unit can estimate the user's emotions and adjust the estimation method based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed estimation information to allow the user to carefully consider the options. If the user is in a hurry, the suggestion unit can provide concise estimation information to allow for a quick selection. Furthermore, if the user is excited, the suggestion unit can provide visually appealing estimation information to capture the user's interest. In this way, the suggestion unit can adjust the estimation method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The proposal department can analyze the user's past estimation history and propose the optimal adjustment method when adjusting estimates. For example, the proposal department can propose the optimal adjustment method based on the user's past estimations. Furthermore, the proposal department can prioritize and propose elements that are frequently adjusted based on the user's past estimation history. In addition, the proposal department can analyze the user's past estimation history and propose the most efficient adjustment method. This allows the proposal department to analyze the user's past estimation history and propose the optimal adjustment method.

[0118] The suggestion function can estimate the user's emotions and prioritize estimates based on those emotions. For example, if the user is stressed, the suggestion function can prioritize displaying important estimate items and postpone others. If the user is relaxed, the suggestion function can display all estimate items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the suggestion function can display the most important estimate items first, enabling them to complete the estimate quickly. In this way, the suggestion function can prioritize estimates based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The proposal function can select the most suitable construction company by considering the user's geographical location when adjusting estimates. For example, if the user lives in a specific area, the proposal function will prioritize displaying construction companies related to that area. Furthermore, if the user is interested in a specific area, the proposal function can prioritize displaying construction companies related to that area. In addition, if the user is considering moving to a specific area, the proposal function can prioritize displaying construction companies related to the living environment of that area. This allows the proposal function to select the most suitable construction company by considering the user's geographical location.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced, and if the user is relaxed, the frequency can be increased. Similarly, if the user is in a hurry, the suggestion timing can be accelerated, and if the user has ample time, the timing can be slower. In this way, the suggestion function can adjust the timing of suggestions based on the user's emotions.

[0122] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can make similar proposals based on designs and requests the user has previously preferred. It can also make more appropriate proposals by avoiding proposals the user has previously rejected. Furthermore, it can predict and propose proposal methods preferred at specific times based on the user's past proposal history. This allows the proposal department to analyze a user's past proposal history and select the most suitable proposal method.

[0123] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions to allow the user to carefully consider their options. If the user is in a hurry, it can provide concise suggestions to allow for quick selection. Furthermore, if the user is excited, it can provide visually appealing suggestions to capture their interest. In this way, the suggestion function can adjust the content of suggestions based on the user's emotions.

[0124] The proposal department can suggest region-specific design elements by considering the user's geographical location. For example, if the user lives in a cold region, it can suggest a design with high insulation performance. If the user lives in a humid region, it can suggest a design with moisture countermeasures. Furthermore, if the user lives in an earthquake-prone region, it can suggest a design with high seismic resistance performance. In this way, the proposal department can suggest region-specific design elements by considering the user's geographical location.

[0125] The suggestion function can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is stressed, important suggestion items will be displayed first, while others will be delayed. If the user is relaxed, all suggestion items will be displayed equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the most important suggestion items will be displayed first, allowing them to complete the suggestions quickly. In this way, the suggestion function can adjust the order of suggestions based on the user's emotions.

[0126] The suggestion department can analyze a user's past suggestion history and determine the priority of suggestions. For example, it can prioritize suggestions that the user has frequently selected in the past. It can also postpone suggestions that the user has previously rejected, thereby providing more appropriate suggestions. Furthermore, it can predict and prioritize suggestions that are preferred during specific time periods based on the user's past suggestion history. In this way, the suggestion department can analyze a user's past suggestion history and determine the priority of suggestions.

[0127] The suggestion function can estimate the user's emotions and adjust the level of detail in its suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions to allow the user to thoroughly consider their options. If the user is in a hurry, it can provide concise suggestions to allow for quick selection. Furthermore, if the user is excited, it can provide visually appealing suggestions to capture their interest. In this way, the suggestion function can adjust the level of detail in its suggestions based on the user's emotions.

[0128] The suggestion function can customize its suggestions based on the user's current living situation. For example, if the user changes their family structure, it can provide suggestions accordingly. Similarly, if the user changes their lifestyle, it can provide suggestions accordingly. Furthermore, if the user starts a new hobby, it can provide suggestions accordingly. In this way, the suggestion function can customize its suggestions based on the user's current living situation.

[0129] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions to allow the user to fully consider their options. If the user is in a hurry, it can provide concise suggestions to allow for quick selection. Furthermore, if the user is excited, it can provide visually appealing suggestions to capture their interest. In this way, the suggestion function can adjust the way it presents suggestions based on the user's emotions.

[0130] The proposal department can analyze users' social media activity and incorporate relevant information into its proposals. For example, it can propose relevant design elements based on information users share on social media. It can also propose relevant design elements based on information about accounts users follow on social media. Furthermore, it can propose relevant design elements based on information users have shown interest in on social media. In this way, the proposal department can analyze users' social media activity and incorporate relevant information into its proposals.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk inputs information about the land, the surrounding area, and the client's intentions. For example, the user can input information such as the land's topography, soil type, drainage conditions, transportation access, surrounding facilities, noise level, design preferences, budget, and functional requirements. Step 2: The proposal department analyzes the information entered by the reception department and proposes multiple designs. For example, it uses AI to generate multiple designs that include different design concepts and cost variations based on user requests, employing data analysis methods and algorithms. Step 3: The revision team adds any points or requests for changes to the design proposed by the proposal team and resubmits the revised proposal. For example, the user inputs changes or additional elements to the proposed design, and the AI ​​generates a revised proposal based on that input. Step 4: The decision-making unit determines the details such as the floor plan and fixtures based on the design modified by the modification unit. For example, the user inputs details of the floor plan, types of fixtures, material selections, etc., and the AI ​​determines the final design based on that information.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, proposal unit, modification unit, and decision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs the land conditions and the client's intentions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes multiple designs using AI. The modification unit is implemented by, for example, the control unit 46A of the smart device 14, which re-proposes a modified design based on the user's requests. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the final design. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, proposal unit, modification unit, and decision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs the land conditions and the client's intentions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes multiple designs using AI. The modification unit is implemented, for example, by the control unit 46A of the smart glasses 214, which re-proposes a modified design based on the user's requests. The decision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which determines the final design. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the reception unit, proposal unit, modification unit, and decision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs the land conditions and the client's intentions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes multiple designs using AI. The modification unit is implemented by, for example, the control unit 46A of the headset terminal 314, which re-proposes a modified design based on the user's requests. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the final design. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the reception unit, proposal unit, modification unit, and decision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs the land conditions and the client's intentions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes multiple designs using AI. The modification unit is implemented by, for example, the control unit 46A of the robot 414, which re-proposes a modified design based on the user's requests. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the final design. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A reception area where you input information about the land, the surrounding area, and the client's intentions, The reception unit analyzes the information entered and proposes multiple designs, The revision department adds any points or requests for modification to the design proposed by the aforementioned proposal department and resubmits the revised plan. The system comprises a determination unit that determines the details such as the floor plan and fixtures based on the design modified by the aforementioned modification unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We quickly incorporate the client's requests and provide careful advice if they are unreasonable. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Using CAD, we provide an image of what the building would actually look like at that location. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We also propose specialist contractors for renovations, repairs, and cleaning, tailored to your budget. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will compare quotes from multiple construction companies and select the best one within the budget. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing and method of input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting information, the system filters the input based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting data, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the design category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline for the designs. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned modification section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned modification section is, When making corrections, we analyze the user's past correction history and suggest the optimal correction method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, During the modification process, the modifications are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, When making corrections, the optimal correction method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, When making corrections, we analyze users' social media activity and propose corrections. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, It estimates the user's emotions and adjusts the decision-making process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, When making a decision, the system analyzes the user's past decision history to suggest the optimal decision-making method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned determination unit, When making a decision, customize the decision based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned determination unit, It estimates the user's emotions and prioritizes decisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned determination unit, When making a decision, the optimal decision-making method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned determination unit, When making a decision, we analyze the user's social media activity and suggest a suitable course of action. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and adjusts the advice given based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When providing advice, we analyze the user's past request history to propose the most suitable advice. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned proposal section is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When providing advice, the system selects the most appropriate advice by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts how images are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When providing images, we analyze the user's past image history and suggest the optimal display method. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned proposal section is, It estimates the user's emotions and determines image priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When providing images, the optimal display method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned proposal section is, It estimates the user's emotions and adjusts the vendor's proposal method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When proposing vendors, we analyze the user's past vendor usage history to provide the most suitable proposal. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned proposal section is, It estimates user sentiment and determines vendor priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When proposing service providers, the most suitable provider will be selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned proposal section is, We estimate the user's emotions and adjust the estimation method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned proposal section is, When adjusting quotes, we analyze the user's past quote history and propose the optimal adjustment method. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the estimates based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned proposal section is, When adjusting estimates, the system selects the most suitable construction company by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you input information about the land, the surrounding area, and the client's intentions, The reception unit analyzes the information entered and proposes multiple designs, The revision department adds any points or requests for modification to the design proposed by the aforementioned proposal department and resubmits the revised plan. The system comprises a determination unit that determines the details such as the floor plan and fixtures based on the design modified by the aforementioned modification unit. A system characterized by the following features.

2. The aforementioned proposal section is, We quickly incorporate the client's requests and provide careful advice if they are unreasonable. The system according to feature 1.

3. The aforementioned proposal section is, Using CAD, we provide an image of what the building would actually look like at that location. The system according to feature 1.

4. The aforementioned proposal section is, We also propose specialist contractors for renovations, repairs, and cleaning, tailored to your budget. The system according to feature 1.

5. The aforementioned proposal section is, We will compare quotes from multiple construction companies and select the best one within the budget. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing and method of input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When inputting information, the system filters the input based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images